Pain inferring device and pain inferring method

ABSTRACT

A pain inferring device for designing a shape giving a subject as little pain as possible when the subject touches the shape. The pain inferring device ( 10 ) includes an input unit ( 11 ), an output unit ( 12 ), a main control unit ( 13 ), a learning storage unit ( 14 ), and a neural network ( 15 ). The neutral network learns the relationship between the input value of shape data and the output value of the data on the degree of pain when a subject touches a shape. By the learning, an input/output function (namely, the coefficient of coupling of neurons in layers) representing the relationship between the input and output values is defined and stored in the learning storage unit ( 14 ). When the shape data is inputted through the input unit ( 11 ), the neural network ( 15 ) infers the degree of pain by using the function stored in the learning storage unit ( 11 ).

TECHNICAL FIELD

[0001] The present invention relates to a pain inferring device, morespecifically to a pain inferring device for inferring a pain felt by aperson when he or she touches a certain shape.

BACKGROUND ART

[0002] Taking a switch for example, operating portion thereof is formedto have an uneven surface so as to prevent slipping of operators'fingers during operation. However, operators are likely to feel paindepending on the shape of the uneven surface or on the manner ofoperating the switch.

[0003] Evaluation of the degree of pain in operators has conventionallybeen practiced by preparing, at the stage of designing an operatingportion, those having various types of uneven surface profiles andallowing test subjects to operate the operating portions thus prepared.An optimum shape is then selected based on evaluation of the degree ofpain or on measurement to prepare an operating portion giving minimizedpain to operators. However, in order to obtain subjective evaluation ofdegrees of pain by test subjects, there are prepared a number ofoperating portions, at the stage of designing, so that it takes muchtime and labor. Thus, it is difficult to easily set out at the stage ofdesigning an operating portion giving as little pain as possible.

DISCLOSURE OF THE INVENTION

[0004] It is an object of the present invention to provide a paininferring device and a pain inferring method capable of designing easilya shape giving minimized pain.

[0005] According to one aspect of the present invention, there isprovided a pain inferring device. The pain inferring device includesinput means, learning means and function storage means. The input meansinputs shape data for specifying a shape. The learning means learns therelationship between the shape data as an input and a degree of pain, asan output, felt by a person when he or she touches the shapecorresponding to the shape data to produce an input-output functionindicative of the relationship between the input and the output. Thefunction storage means stores the input-output function. As soon as theshape data are inputted through the input means, the learning meansinfers the degree of pain based on the input-output function stored inthe function storage means.

[0006] According to another aspect of the present invention, there isprovided a pain inferring method. The pain inferring method includes thesteps of learning a relationship between shape data as an input and adegree of pain, as an output, felt by a person when he or she touchesthe shape corresponding to the shape data; producing an input-outputfunction indicative of the relationship between the input and theoutput; and inferring based on an input of new shape data a degree ofpain according to the input-output function.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The invention, together with objects and advantages thereof, maybest be understood by reference to the following description of thepresently preferred embodiments together with the accompanying drawingsin which:

[0008]FIG. 1 is a schematic block diagram of a pain inferring deviceaccording to one embodiment of the present invention;

[0009]FIG. 2 is a schematic diagram of a neural network of the paininferring device of FIG. 1;

[0010]FIG. 3 is a schematic diagram of a test piece having a surfaceprofile similar to that of a switch knob;

[0011]FIG. 4 is a graph showing the relationship between each test pieceand the degree of pain in an inferred pain verification test;

[0012]FIG. 5 is a schematic diagram of a neural network of the paininferring device according to a second embodiment of the presentinvention;

[0013]FIG. 6 is a schematic diagram of a test piece having a surfaceprofile similar to that of a switch knob; and

[0014]FIG. 7 is a graph showing the relationship between each test pieceand the degree of pain in an inferred pain verification test.

BEST MODE FOR CARRYING OUT THE INVENTION

[0015] A pain inferring device 10 according to a first embodiment of thepresent invention will be described below referring to FIGS. 1 to 4.

[0016] As shown in FIG. 1, the pain inferring device 10 is provided withan input unit 11, an output unit 12, a main control unit 13, a learningstorage unit 14 and a neural network 15. The main control unit 13 isconnected to the input unit 11, the output unit 12, the learning storageunit 14 and the neural network 15. The input unit 11 consistsessentially of a keyboard and the like and is used by operators forinputting numerical data including shape data etc. The main control unit13 includes a central processing unit (CPU), which performs dataprocessing and control of the input unit 11, the output unit 12, thelearning storage unit 14 and the neural network 15. The output unit 12includes a printer and/or a display unit, which outputs data (inferredvalue) indicative of the degree of pain under control by the maincontrol unit 13.

[0017] The neural network 15 functions as learning means and ispreferably a hierarchical neural network having an input layer, anintermediate layer and an output layer, each layer including amultiplicity of neurons. The coefficients of coupling between neurons ofthe input layer and those of the intermediate layer and between neuronsof the intermediate layer and that of the output layer are learnedpreferably in accordance with the known back-propagation algorithm.Here, the back-propagation algorithm may be replaced with any otherlearning algorithms, so long as its accuracy is permissible. Learning bythe neural network 15 will be described later.

[0018] The learning storage unit 14, which is preferably a non-volatilesemiconductor memory, stores information including the number of layersin the neural network 15, the number of synapses connecting the neuronsof the respective layers and coefficients of coupling between thesynapses. The information stored in the learning storage unit 14 ishandled as an input-output function governing the relationship betweenthe input values and the output value.

[0019] The pain inferring device 10 is used, for example, for designinga switch knob (not shown) having a surface profile similar to that of atest piece 22 as shown in FIG. 3. The test piece 22 has on the surfacethereof a plurality of ribs 20 arranged parallel to one another.

Shape Data Setting

[0020] Shape data (hereinafter referred to as shape variables) of theribs 20 include the height h, the rib-to-rib pitch S, the radius ofcurvature R at the crest of the rib 20 and the width b of the rib 20.The shape variables are set based on the case where the ribs 20 areformed on a test piece 22 having a length t (30 mm, in this embodiment).Data of nine types of test pieces having ribs 20 with different shapevariables are shown in Table 1. TABLE 1 Test piece no. 1-1 1-2 1-3 1-41-5 1-6 1-7 1-8 1-9 H 0.5 0.5 0.5 1.0 1.0 1.0 1.5 1.5 1.5 B 1.0 1.5 2.01.0 1.5 2.0 1.0 1.5 2.0 R 0.0 0.3 0.5 0.3 0.5 0.0 0.5 0.0 0.3 S 0.5 1.01.5 1.5 0.5 1.0 1.0 1.5 0.5

Pain Evaluating Test

[0021] There were provided test pieces 22 based on the nine types ofdata shown in Table 1, and a pain evaluating test (organoleptic test)was performed using five adult males as test subjects.

[0022] Evaluation of pain is performed by calculating the absoluteevaluation point using the known Scheffe's paired comparison method(variation of the Haga's method). According to the paired comparisonmethod, each test subject places his or her fingers on a pair of testpieces fixed parallel to each other, and in this state the test piecesare drawn back horizontally from the test subject. The test subjectevaluates degrees of pain felt by himself or herself at that momentaccording to the 7 point rating method (rating in the range of 0±3, byan increment or decrement of 0.5 point) (see “Degree of pain beforenormalization” in Table 2).

[0023] Mean values of these evaluation points for the test pieces arecalculated and normalized, respectively. Here, normalization of the meanvalues is performed as follows:

[0024] First, the maximum theoretical value A of the absolute evaluationpoint is determined according to the following equation (1):

A=α×(Number of samples evaluated−1)/Number of samples evaluated  (1)

[0025] wherein α represents the maximum value of the relative evaluationpoint. Here, the minimum theoretical value is also determined.Approximate values 1 and 0 are set referring to the maximum theoreticalvalue A and the minimum theoretical value of the absolute evaluationpoint obtained according to the above equation (1) to performnormalization based on the approximate values 1 and 0.

[0026] It should be noted here that, in this test, nine test pieces wereused as samples to be evaluated, so that the maximum theoretical valueand the minimum theoretical value were calculated to be 2.67 and −2.67according to the above equation (1), and the approximate values of themaximum value and the minimum value were set at 2.5 and −2.5,respectively, to perform normalization based on these values. Degrees ofpain before and after normalization are shown below in Table 2. TABLE 2Test piece no. 1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 Degree of pain before−1.42 −0.04 0.53 1.11 −0.11 −0.80 1.69 −0.07 −0.89 normalization Degreeof pain after 0.22 0.49 0.61 0.72 0.48 0.34 0.84 0.49 0.32 normalization

[0027] The evaluation point xijl of the assay test piece (one test pieceof the two) in comparison with the reference test piece (the other testpiece of the two) is assumed according to the following equation (2)

xijl=(αi−αj)+gij+eijl  (2)

[0028] In the above equation,

[0029] i: assay test piece no.;

[0030] j: reference test piece no.;

[0031] αi: absolute evaluation point of i;

[0032] αj : absolute evaluation point of j;

[0033] gij: effect to be exhibited by the combination of reference testpiece and assay test piece;

[0034] l: test subject no.; and

[0035] eijl: error of test subject

[0036] The effect gij to be exhibited by the combination of thereference test piece and the assay test piece is a psychologicalafterimage effect of evaluation of the previous test piece on theevaluation of the subsequent test piece. Thus, absolute evaluation pointαi of each test piece is calculated according to the equation (2).

Learning by Pain Inferring Device 10

[0037] The shape variables as input values and the absolute evaluationpoint αi as an output value are inputted through the input unit 11 ofthe pain inferring device 10 to allow the neural network 15 to learn therelationship between the input and the output. The neural network 15,which is of 4-input and 1-output system, includes an input layer, anintermediate layer and an output layer, as shown in FIG. 2, and learnsaccording to the back-propagation algorithm. The intermediate layer hastwo elements (neurons). Before inputting to the input unit 11, the inputvalues and the output value are normalized such that they have a maximumvalue of 0.95 and a minimum value of 0.05. The learning by the neuralnetwork 15 is terminated when the error of the output value reduces to atolerable level of 0.01 or less. The input-output function governing thelearned relationship between the input and the output is stored in thelearning storage unit 14. It should be noted here that, referring to thedegree of pain, the greater the numerical value, the greater the degreeof pain, whereas the smaller the numerical value, the smaller the degreeof pain. If shape variables are input to the pain inferring device 10after completion of learning, the device 10 can infer the degree of painto be given by the shape corresponding to the shape variables.

Inferred Pain Verification Test

[0038] The degree of pain inferred, after completion of learning, by thepain inferring device 10 (inferred value) was compared with degrees ofpain reported by a plurality of (in this embodiment, five adult males)test subjects as the result of an organoleptic test (organolepticvalues).

[0039] In the verification test, there were prepared seven test pieceseach having a shape according to shape variables, which are differentfrom those used in the pain evaluating test. Each test piece wassubjected to organoleptic test, whereas the shape variables were inputto the pain inferring device 10 to determine the degree of pain. In thepain inferring device 10, as soon as the shape variables are inputthereto, the neural network 15 supplies the degree of pain in accordancewith the input-output function stored in the learning storage unit 14 tothe main control unit 13. In the organoleptic test, degrees of pain weredetermined according to the equation (2) to calculate a mean value likein the pain evaluating test.

[0040] Table 3 shows shape variables of each test piece, and Table 4shows an inferred value as the degree of pain in each test piecemeasured by the pain inferring device 10 after learning and a normalizedvalue of the degree of pain in each test piece evaluated by theorganoleptic test (organoleptic value). Here, the normalization wasperformed such that the maximum value and the minimum value beforenormalization are set at 1 and 0 respectively. TABLE 3 Test piece no.1-10 1-11 1-12 1-13 1-14 1-15 1-16 H 1.5 1.5 1.5 1.5 1.5 0.5 0.5 B 1.52.0 2.0 1.0 1.0 1.0 1.0 R 0.0 0.0 0.5 0.5 0.5 0.0 0.5 S 0.5 0.5 0.5 0.51.5 1.5 1.5

[0041] TABLE 4 Test piece no. 1-10 1-11 1-12 1-13 1-14 1-15 1-16 Degreeof pain 0.00 0.02 0.31 0.63 1.00 0.29 0.92 (organoleptic Value) Degreeof pain 0.01 0.00 0.34 0.69 1.00 0.43 0.89 (inferred value)

[0042]FIG. 4 is a graph showing the relationship between each test pieceand the degree of pain in the inferred pain verification test.

[0043] As is clear from FIG. 4, there was obtained a result that theinferred values as the degrees of pain measured by the pain inferringdevice 10 substantially coincide respectively with the organolepticvalues obtained as the degree of pain in the organoleptic test.

Second Embodiment

[0044] Next, the pain inferring device according to a second embodimentof the present invention will be described referring to FIGS. 5 to 7.

[0045] The pain inferring device 10 of the second embodiment is providedwith an input unit 11, an output unit 12, a main control unit 13, alearning storage unit 14 and a neural network 15. The neural network 15in the second embodiment is of 2-input and 1-output system and includesan input layer, an intermediate layer and an output layer, as shown inFIG. 5. The intermediate layer has two elements (neurons).

[0046] The pain inferring device 10 of the second embodiment is suitablefor designing a switch knob (not shown) having a serrated surface, asshown in FIG. 6. In other words, a plurality of ribs 20 each having atriangular cross section are formed parallelwise on the surface of atest piece 22 in the second embodiment.

Shape Data Setting

[0047] Shape variables of the ribs 20 include the height h and the apexangle θ thereof. The shape variables are set based on the case where theribs 20 are formed on a test piece 22 having a length t (30 mm, in thisembodiment). Data of seven types of test pieces having ribs 20 withdifferent shape variables are shown in Table 5. TABLE 5 Test piece no.2-1 2-2 2-3 2-4 2-5 2-6 207 Before h 0.5 0.5 1.0 1.0 1.0 1.5 1.5 (mm)normalization θ 62 152 74 103 136 62 152 (deg) After h 0.05 0.05 0.500.50 0.50 0.95 0.95 normalization θ 0.05 0.05 0.17 0.46 0.50 0.05 0.95

Pain Evaluating Test

[0048] A pain evaluating test (organoleptic test) was performed usingfive adult male test subjects like in the first embodiment for seventest pieces 22. The results are shown in Table 6.

[0049] In the method of normalizing mean values in the secondembodiment, the maximum theoretical value and the minimum theoreticalvalue of the absolute evaluation point were determined according to theequation (1), and the approximate values 1 and 0 were set referring tothe maximum theoretical value and the minimum theoretical value toperform normalization.

[0050] In the organoleptic test, seven test pieces are used as samplesto be evaluated, so that there are obtained the maximum theoreticalvalue 2.5 and the minimum theoretical value −2.5 according to theequation (1). However, if the above maximum theoretical value and theminimum theoretical values are used, the data are very likely toconcentrate. Therefore, normalization was performed employing a maximumvalue of 1.5 and a minimum value of −1.5 therefor. TABLE 6 Test pieceno. 2-1 2-2 2-3 2-4 2-5 2-6 2-7 Degree of −1.37 −0.49 0.26 0.77 0.370.89 −0.43 pain before normalization Degree of 0.04 0.34 0.59 0.76 0.620.80 0.36 pain after normalization

Learning by Pain Inferring Device 10

[0051] The shape variables (normalized values) shown in Table 5 as inputvalues and the absolute evaluation point αi as an output value wereinputted through the input unit 11 of the pain inferring device 10 toallow the neural network 15 to learn the relationship between the inputand the output. The neural network 15 in the second embodiment learnedaccording to the back-propagation algorithm. The input values and theoutput value were normalized such that they have the maximum value of0.95 and the minimum value of 0.05, and the learning by the neuralnetwork 15 was terminated when the error of the output value reduced toa tolerable level of 0.01 or less. If shape variables of a certainserrated shape are inputted to the pain inferring device 10 aftercompletion of learning, the device 10 infers the degree of pain to begiven by the shape corresponding to the shape variables.

Inferred Pain Verification Test

[0052] After completion of learning, the inferred values as the degreesof pain measured by the pain inferring device 10 were compared with thedegrees of pain reported by a plurality of (in this embodiment, fiveadult males) test subjects as a result of an organoleptic test(organoleptic values). In the verification test, there were preparedtest pieces each having a shape according to shape variables which aredifferent from those used in the pain evaluating test, to obtainorganoleptic values as the degrees of pain to be given by the test pieceevaluated by the organoleptic test and inferred values as the degrees ofpain measured by the pain inferring device 10. The organoleptic valuesas the degrees of pain in the organoleptic test were calculatedaccording to the equation (2).

[0053] Table 7 shows shape variables of each test piece before and afternormalization respectively. Table 8 shows inferred values obtained asthe degrees of pain given by the respective test pieces and theorganoleptic values obtained as the degrees of pain in the organoleptictest, after normalization. Here, the normalization of the variables inTable 7 were performed based on the maximum value 0.95 and the minimumvalue 0.5 set in the shape variables in Table 5. The normalization inTable 8 was performed such that the maximum values and the minimumvalues of the organoleptic values, as well as, the inferred valuesbefore normalization are set at 1 and 0, respectively. TABLE 7 Testpiece no. 2-8 2-9 2-10 2-11 2-12 2-13 2-14 Before normalization h (mm)0.80 0.90 0.50 1.50 0.80 1.20 1.50 θ (deg) 53.13 118.07 83.97 86.0586.30 109.56 126.87 After normalization h 0.32 0.41 0.05 0.95 0.32 0.680.95 θ −0.04 0.61 0.27 0.29 0.29 0.53 0.70

[0054] TABLE 8 Test piece no. 2-8 2-9 2-10 2-11 2-12 2-13 2-14 Degree ofpain 0.00 0.85 0.05 1.00 0.59 0.94 0.89 (organoleptic value) Degree ofpain 0.02 0.85 0.00 0.99 0.49 1.00 0.81 (inferred value)

[0055]FIG. 7 is a graph showing test piece nos. and the degrees of painin the inferred pain verification test.

[0056] As is clear from FIG. 7, there was obtained a result that theinferred values as the degrees of pain measured by the pain inferringdevice 10 substantially coincide with the organoleptic values obtainedas the degree of pain in the organoleptic test.

[0057] The pain inferring device 10 according to the present inventionenjoys the following advantages:

[0058] (1) The pain inferring device 10 includes the input unit 11 forinputting shape variables (shape data) for specifying a shape, theneural network 15 learning the relationship between the input of theshape data and the output of the degree of pain to be given by a shapecorresponding to the shape data to produce an input-output functionindicative of the relationship between the input and the output, and thelearning storage unit 14 for storing the input-output function. If shapevariables are inputted through the input unit 11, the main control unit13 supplies to the output unit 12, a degree of pain obtained from theneural network 15 based on the shape variables and the function. Thus,by inputting various kinds of shape variables to the pain inferringdevice 10, the degree of pain associated with the shape variables caneasily be inferred by the device 10 at the stage of designing. In otherwords, once the pain inferring device 10 learns degrees of pain, thereis no need of preparing a test piece having a new shape for measuringthe degree of pain thereof.

[0059] (2) Learning by the neural network 15 easily enables inferring ofthe degree of pain; and

[0060] (3) The neural network 15 produces an input-output functionindicative of the relationship between known shape variables (shapedata) as input values and the data on the degree of pain associated withthe shape variables, as an output value, and if new shape variables areinputted to the pain inferring device 10, the device 10 infers thedegree of pain based on the input-output function. According to thisinferring method, the degree of pain can easily be inferred.

[0061] It should be apparent to those skilled in the art that thepresent invention may be embodied in many other specific forms withoutdeparting from the spirit or scope of the invention. Particularly, thepresent invention may be embodied in the following forms

[0062] The hierarchical neural network 15 may be replaced with aninterconnecting neural network.

[0063] The hierarchical neural network 15 may have two or moreintermediate layers.

[0064] The surface profile of the test piece is not to be limited to theribs 20, but the ribs 20 may be replaced, for example, with steps orprotrusions. The protrusion may each have, for example, a truncatedquadrangular pyramidal shape, a truncated conical shape or ahemispherical shape.

1. A pain inferring device, comprising: input means (11) for inputting shape data specifying a shape; learning means (15) for learning a relationship between the shape data as an input and a degree of pain, as an output, felt by a person when he or she touches the shape corresponding to the shape data and producing an input-output function indicative of the relationship between the input and the output; and function storage means (14) for storing the input-output function, wherein the learning means infers the degree of pain based on the input-output function stored in the function storage means.
 2. The pain inferring device according to claim 1, wherein the learning means comprises a neural network.
 3. The pain inferring device according to claim 2, wherein the neural network learns the relationship between the input and the output according to a back-propagation algorithm.
 4. The pain inferring device according to claim 3, wherein the neural network inputs a predetermined shape data and a predetermined degree of pain in the back-propagation algorithm and terminates the learning when an error between a degree of pain produced newly and the predetermined degree of pain reduces to a predetermined level or lower.
 5. The pain inferring device according to any one of claims 1 to 4, further comprising: output means (12) for outputting the degree of pain; and control means (13) for controlling the output means to output, when certain shape data is inputted by the input means, a degree of pain acquired by the learning means based on the function stored in the function storage means.
 6. A pain inferring method, comprising the steps of: learning a relationship between shape data as an input and a degree of pain, as an output, felt by a person when he or she touches a shape corresponding to the shape data; producing an input-output function indicative of the relationship between the input and the output; and inferring, based on an input of new shape data, a degree of pain according to the input-output function.
 7. The pain inferring method according to claim 6, wherein the learning step and the inferring step are performed using a neural network.
 8. The pain inferring method according to claim 7, wherein the learning step includes learning the relationship between the input and the output in accordance with a back-propagation algorithm using the neural network.
 9. The pain inferring method according to claim 8, further comprising the steps of: inputting predetermined shape data and a predetermined degree of pain; and terminating learning by the neural network, when an error between a degree of pain formed newly and the predetermined degree of pain reduces to a predetermined level or lower in the back-propagation algorithm. 